Machine Learning of the Reactor Core Loading Pattern Critical Parameters
The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is hig...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2008-01-01
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| Series: | Science and Technology of Nuclear Installations |
| Online Access: | http://dx.doi.org/10.1155/2008/695153 |
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| _version_ | 1832553044902936576 |
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| author | Krešimir Trontl Dubravko Pevec Tomislav Šmuc |
| author_facet | Krešimir Trontl Dubravko Pevec Tomislav Šmuc |
| author_sort | Krešimir Trontl |
| collection | DOAJ |
| description | The usual approach to loading pattern optimization involves high degree of
engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code
used for evaluating proposed loading patterns. The speed of the optimization process is highly
dependent on the computer code used for the evaluation. In this paper, we investigate the
applicability of a machine learning model which could be used for fast loading pattern evaluation. We
employ a recently introduced machine learning technique, support vector regression (SVR), which
is a data driven, kernel based, nonlinear modeling paradigm, in which model parameters are
automatically determined by solving a quadratic optimization problem. The main objective of the
work reported in this paper was to evaluate the possibility of applying SVR method for reactor core
loading pattern modeling. We illustrate the performance of the solution and discuss its applicability,
that is, complexity, speed, and accuracy. |
| format | Article |
| id | doaj-art-2d2c486922ea45c98dc18cd26aa252f5 |
| institution | Kabale University |
| issn | 1687-6075 1687-6083 |
| language | English |
| publishDate | 2008-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Science and Technology of Nuclear Installations |
| spelling | doaj-art-2d2c486922ea45c98dc18cd26aa252f52025-02-03T05:57:08ZengWileyScience and Technology of Nuclear Installations1687-60751687-60832008-01-01200810.1155/2008/695153695153Machine Learning of the Reactor Core Loading Pattern Critical ParametersKrešimir Trontl0Dubravko Pevec1Tomislav Šmuc2Department of Applied Physics, Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, CroatiaDepartment of Applied Physics, Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, CroatiaDivision of Electronics, Ruđer Bošković Institute, Bijenička 54, 10002 Zagreb, CroatiaThe usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper, we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employ a recently introduced machine learning technique, support vector regression (SVR), which is a data driven, kernel based, nonlinear modeling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modeling. We illustrate the performance of the solution and discuss its applicability, that is, complexity, speed, and accuracy.http://dx.doi.org/10.1155/2008/695153 |
| spellingShingle | Krešimir Trontl Dubravko Pevec Tomislav Šmuc Machine Learning of the Reactor Core Loading Pattern Critical Parameters Science and Technology of Nuclear Installations |
| title | Machine Learning of the Reactor Core Loading Pattern Critical Parameters |
| title_full | Machine Learning of the Reactor Core Loading Pattern Critical Parameters |
| title_fullStr | Machine Learning of the Reactor Core Loading Pattern Critical Parameters |
| title_full_unstemmed | Machine Learning of the Reactor Core Loading Pattern Critical Parameters |
| title_short | Machine Learning of the Reactor Core Loading Pattern Critical Parameters |
| title_sort | machine learning of the reactor core loading pattern critical parameters |
| url | http://dx.doi.org/10.1155/2008/695153 |
| work_keys_str_mv | AT kresimirtrontl machinelearningofthereactorcoreloadingpatterncriticalparameters AT dubravkopevec machinelearningofthereactorcoreloadingpatterncriticalparameters AT tomislavsmuc machinelearningofthereactorcoreloadingpatterncriticalparameters |